kavyamanohar/whisper-supreme-court-asr
This model is a fine-tuned version of openai/whisper-small on the Supreme Court Hearing Corpus - Subset dataset. It achieves the following results on the evaluation set:
- Loss: 1.8703
- Wer: 69.8671
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.8763 | 5.5714 | 100 | 1.8904 | 162.7770 |
| 0.7801 | 11.1143 | 200 | 1.5403 | 55.0222 |
| 0.0716 | 16.6857 | 300 | 1.5505 | 56.7947 |
| 0.0118 | 22.2286 | 400 | 1.6573 | 56.8685 |
| 0.0076 | 27.8 | 500 | 1.7218 | 83.3087 |
| 0.0014 | 33.3429 | 600 | 1.7909 | 79.2467 |
| 0.0005 | 38.9143 | 700 | 1.8290 | 67.8730 |
| 0.0003 | 44.4571 | 800 | 1.8555 | 69.4239 |
| 0.0002 | 50.0 | 900 | 1.8669 | 69.7932 |
| 0.0002 | 55.5714 | 1000 | 1.8703 | 69.8671 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0